Truck platooning reshapes greenhouse gas emissions of the integrated vehicle-road infrastructure system

Reducing greenhouse gas emissions has turned into a pillar of climate change mitigation. Truck platooning is proposed as a strategy to lower emissions from vehicles on roads. However, the potential interactive impacts of this technology on road infrastructure emissions remain unclear. Here, we evaluate the decarbonization effects of truck platooning on the integrated vehicle-road system at a large-scale road network level, spanning 1457 road sections across North America. We show that truck platooning decreases emissions induced by truck operations, but it degrades faster the durability of road infrastructure and leads to a 27.9% rise in road emissions due to more frequent maintenance work. Overall, truck platooning results in a 5.1% emission reduction of the integrated vehicle-road system. In contrast to the benefits of emission reduction, truck platooning leads to additional financial burdens on car users and transportation agencies, calling for the consideration of tradeoffs between emissions and costs and between agencies and users. Our research provides insights into the potential applications of truck platooning to mitigate climate change.


Supplementary Method 1. Acquisitions of road information data based on the LTPP's database 1.1 Introduction of the LTPP database
The monitoring data of the road sites, traffic volumes, environmental conditions, road performance, and road maintenance/rehabilitation are obtained from the Long-term Pavement Performance (LTPP) program. The LTPP program, which is initiated by the U.S. Federal Highway Administration (FHWA) and the American Association of State Highway and Transportation Officials (AASHTO), aims to study the performance of in-service roads via both field observations and laboratory tests [1] . It is designed to understand how and why roads perform as they do [2] . To achieve this goal, the LTPP program has monitored, recorded, and accumulated a great amount of road performance-related data since 1987. The data is accessible through a comprehensive information management system (IMS) [2] , which consists of a road performance database, LTPP traffic analysis software database, and ancillary information management system archives. The LTPP's road performance database is the main source of data for this study. Data in the database can be downloaded from the website (https://infopave.fhwa.dot.gov/Data/DataSelection).
In-situ road sections located in the U.S. and Canada are chosen and investigated in the LTPP program. Those road sections are designed and constructed by following the standard practices of the highway administration agencies, and they are subject to real-life environmental and traffic conditions. The performance-related data of the LTPP test sections include road structure and construction details, climate conditions, traffic conditions, and road performance. Approximately 280 million records of data have been collected so far, and the number is still climbing. The LTPP's database constitutes the world's largest and most comprehensive road performance database [2] .

Road sites under study
A total of 1,457 road sections from the LTPP program are investigated in this study. The sections are selected based on two principles: 1) The road sections are paved with asphalt concrete. This is because the analyzed truck traveling mode (i.e., regular mode or platoon-oriented mode) apparently affects the performance of the asphalt layer due to the visco-elastic nature of asphalt concrete material.
2) Daily traffic data of the chosen road section should be available. This research aims to analyze the potential effects of truck platooning on the performance and Greenhouse Gas (GHG) emissions from road infrastructure. Therefore, traffic data is crucial for analyzing the travel behaviors of the trucks as well as the damage evolution of the road. However, some LTPP road sections lack traffic data, which are thus excluded from the analysis.
The locations of the 1,457 road sections are plotted in Fig. 1-1. Also shown in the figure is the elevation information. It is seen that the chosen road sections are located at various states in the U.S. and certain provinces in Canada (61 states/districts/provinces and 350 counties). These sections cover all four climate zones classified by FHWA: Dry-Freeze (DF), Wet-Freeze (WF), Dry-Nonfreeze, and Wet-Nonfreeze (WNF) [3,4] . The wide coverage of these sections makes them representative of the road transportation conditions in North America. Besides geographical locations, the assigned dates (i.e., the dates when the monitoring starts) for the road sections are also extensive. Fig. 1-2 shows the histogram of the assigned dates of road sections understudy. It is noted that the assigned dates of the sections range widely from 1990.01 to 2020.01. More than 80% of road sections have been monitored since 1990, and this number has increased to 95% since 2000. The above fact reveals that the performance-related data of the roads cover more than 30 years. The selected road sections also cover various functional classifications, which are defined by their roles in connecting traffic. For instance, some roadways are designed to connect the traffic flows between urban areas across the state, and their functions are classified as Urban Principal Arterial -Interstate. The LTPP data contains a total of 12 functional classifications, 11 of which are covered in this study. The distribution of the functional classifications of chosen road sections is summarized in Table 1-1. Roads with different functional classifications generally experience different levels of traffic and construction/maintenance investment. Therefore, the inclusion of various functional classifications in the research is beneficial in investigating the impacts of truck platooning on different grades of roadways.

The structures and materials of the road sections
Information on the structures and materials of the investigated road sections is retrieved from the database. A schematic diagram of the pavement structures and materials is illustrated in Fig. 1-3. Each road section has its own number of layers, layer types, layer thickness, and materials used for each layer. Material properties such as the modulus and Poisson's ratio of each layer are also needed. Together, the road structure and material properties are used to analyze its mechanical behaviors under the combined effects of truck loads and environmental conditions. Such information is also necessary for calculating GHG emissions associated with road construction. An example of road structure data is presented in Table 1  Asphalt content and density data for the relevant road layers are also shown in Table 1 Where, E is the dynamic modulus (Pa),  ,  ,  and  are the fitting parameters,  is the frequency at the reference temperature (Hz), and the frequency at another temperature is calculated by multiplying a shift factor. The model for calculating the shift factor is: Where, T  is the shift factor, T is the temperature, and 1  , 2  , 3  are fitting parameters.
The parameters for the sigmoidal models of most AC layers can be found in the LTPP database. For those road sections missing such data, the averaged results are assigned to the AC layers of the sections, as shown in Table 1-3.

Traffic data
Traffic information in the LTPP database is obtained from the historical data of the sections before assignment or the monitoring data of the section after assignment. The LTPP program has collected the traffic data of the road sections since 1990. In this research, two types of data stored in the traffic module are used: the daily traffic data of the section and the gross vehicle weights (GVW) of the trucks. The daily traffic data is mainly used to simulate the traffic flow on the section under the regular traffic mode and the truck platooning mode. It presents the hourly traffic data on the sections within a day, including the recording hour, the vehicle classification, and the corresponding vehicle number. In LTPP's traffic module, a total of 13 types of vehicles as classified by the FHWA are monitored. More details regarding the hourly traffic data and their applications will be introduced in Supplementary Method 2.
The GVWs of the trucks are used to calculate the road's damage state, which further forms a basis for determining the maintenance activities. In LTPP, weigh-in-motion (WIM) facilities are used to measure representative axle loads on some road sections to calculate GVW. Due to the high costs of installing and operating WIM facilities, however, some sections lack such facilities. For those sections, GVW data from the nearest road sections are assigned. Similar to daily traffic data, the GVW data also covers different vehicle classifications. Fig. 1-4 plots the GVWs of one type of single truck (FHWA vehicle classification 5) and one type of combo truck (FHWA vehicle classification 11) on 1,457 road sections for illustration purposes.

Climatic data
Climate data in the LTPP database include precipitation, temperature, wind, and humidity. Such information is obtained from the U.S. National Climatic Data Center (NCDC) and the Canadian Climate Center (CCC) [1] . The temperature should be paid closer attention to as it predominantly influences the modulus as well as the damage state of the AC layer under traffic load.
In the LTPP program, thermometers are installed in some road sections to monitor the temporal (daily, seasonal, and annual) variations of road temperatures. These field-measured temperature data are extracted from the database to characterize the climatic conditions of the roads. Field measurements suggest that pavement temperature is dependent on its geographical location (i.e., climatic region).  Besides pavement surface temperature variations associated with different climatic zones, pavement depth also affects temperature. The temperature distributions along the depth profile for the two roads in Texas and Ontario are shown in Fig. 1-6. Distinguished temperature gradients can be found along the depth profiles of both roads. Therefore, in characterizing the temperatures of different pavement layers, proper temperature gradients are needed. The results in Fig. 1-5 and Fig. 1-6 indicate that it is essential to assign a proper temperature field (i.e., the temperature distribution in the depth profile) to each road section. In this research, field-measured data are used to assign the temperature field for road sections with monitored temperature data. For the road sections without such data, temperature data from the nearest roads are used, considering that the neighboring roads are subjected to similar environmental conditions. Eventually, temperature fields are assigned to all of the 1,457 road sections. Those temperature data are used to determine the road responses and road damage states under truck loads. The detailed applications of the temperature data will be introduced in Supplementary Method 4.

Road performance data
A primary road performance indicator is pavement roughness, which is not only related to riding comfort but also affects fuel consumption. Hence, pavement roughness is evaluated in this research. Pavement roughness is typically represented by an indicator called the International Roughness Index (IRI). Although IRI is extensively monitored in the LTPP program, there are gaps in the monitoring data. For example, Fig. 1-7 (a) presents the recorded IRI values of a road section (State Code=1 and SHRP ID=4155) at different service years. IRI data are missing after construction, in the middle of the monitoring period, and at the end of service life. Fig. 1-7 (a) also indicates that the IRI value drops after road maintenance, but the IRI values immediately before and after the maintenance are also missing. To fill in the missing data, exponential regression models are developed from the existing data, and the missing data are predicted by the models. Fig. 1-7 (b) illustrates the supplementary IRI data after filling in the missing data. Overall, all the necessary IRI data are well predicted. The complete IRI data is used to calculate the GHG emissions during road operation, and the detailed calculation procedure will be introduced in Supplementary Method 5.

Road maintenance/rehabilitation data
Maintenance or rehabilitation work is frequently conducted to keep the road in good condition. The commonly used maintenance/rehabilitation methods include overlay, milling of existing pavement layers, surface patching, and cracking sealing. However, additional emissions are generated by the vehicle-road infrastructure system due to maintenance/rehabilitation activities and interruption to traffic. Therefore, maintenance/rehabilitation work conducted on a road section is fully considered in analyzing its life-cycle emission. The maintenance/rehabilitation data are extracted from the LTPP database. The types and records of maintenance/rehabilitation activities are summarized in Table 1-6. With such data, emissions associated with road maintenance/rehabilitation work are calculated. The corresponding calculation procedure will be introduced in Supplementary Method 5.

Supplementary Method 2. Traffic flow simulation of truck platooning
Two types of traffic modes are included for analysis: normal operation and truck platooning. In normal operation, trucks run separately on the road, and adjacent trucks are not intentionally connected. In platooning, a rearward truck intentionally closely follows a preceding truck, and the distance between the adjacent trucks is kept narrow (about 5 m) and stable. Hence, those trucks are closely connected and run in a platoon. Distances between two trucks are not only related to the fuel consumption of trucks, but they also affect the loading mode (i.e., loading interval) on the road pavements and further their durability. We developed a traffic flow simulation framework to determine the spatial-temporal distributions of vehicles under the two traffic modes. The outputs of the simulation are then used to assess road durability as well as the fuel consumption of vehicles. The traffic simulation method is introduced in detail as follows.

The overall framework of traffic simulation
The overall framework of traffic simulation is presented in Fig. 2-1, which includes three main sections:  Preparation The preparation section deals with collecting road and traffic data for simulation and selecting a proper traffic flow model to describe the travel behaviors of vehicles.  Simulation In this section, the travel behaviors of vehicles are simulated using the input data and traffic flow model. Both normal operation and the platoon-oriented traffic mode are simulated. These two modes differ in the travel behaviors of trucks and the overall traffic dynamics.
 Results The result section shows the output data from the simulation, including vehicle ID, travel time and location, speed, and acceleration/deceleration. Based on these outputs, the traffic flow of a road section is generated. The information is used to calculate vehicle emissions and to determine the loading gap between trucks, which is further used to calculate pavement responses and damage accumulation.

Traffic information
As illustrated in Fig  The representative sizes and front areas of those vehicles used for simulation are also presented in Table 2-1. Vehicle dimensions in Table 2-1 are based on statistical data collected from truck manufacturers or truck dealers [5][6][7] .

Traffic flow models
The traffic flow models describing the normal operation and truck platooning are introduced as follows.

The model for normal operation
An improved Intelligent Driver Model (IDM) is used to characterize the behaviors of trucks and cars in normal operation. The IDM, initially proposed by Treiber et al. [8] , is widely used in microscopic traffic simulation. Later, Ye and Zhang [9] found that the impact of the car-truck interaction on traffic flow is significant. They proposed that four types of car-truck interactions need to be taken into account in simulating the heterogeneous car-truck traffic flow: car-following-car (CC), car-following-truck (CT), truck-following-car (TC), and truck-following-truck (TT). To consider the heterogeneity of traffic flow, an improved IDM is further proposed by Treiber et al. [10] , as expressed Equation (2.1).
Where, a is the maximum acceleration, v is the actual velocity, V is the desired velocity,  is the acceleration exponent, ( ) S  is the desired minimum gap, ,0 n s and ,1 n s are the jam distances,  is the safe time headway, b is the desired deceleration, L is the leading vehicle length, and is the velocity difference between the vehicle n and its preceding vehicle 1 n  , which is calculated using Equation (2.2).
Each parameter (i.e., a ,  , V , 0 s , 1 s ,  and b ) has four alternatives. For instance, a can be CC a , CT a , TC a and TT a , which correspond to the accelerations of four types of car-truck combinations, respectively. The leading vehicle length L has two alternatives, namely c L and t L , which correspond to the lengths of the car and truck, respectively.
The input parameters for the improved IDM are summarized in Table 2-3. Those parameter values are calibrated by using the Next Generation SIMulation (NGSIM) trajectory data with high time resolution [11] . Specifically, the vehicle trajectory data collected at Hollywood Freeway (U.S.101) and Berkeley Highway (I-80) in California are used for calibration. Consequently, the adopted parameters are regarded to reflect the realistic traffic flow characteristics of the U.S. highway.  [11] .

The model for truck platooning
The platooning trucks in the model are operated with two sub-controllers that are designed for different control objectives [12] : • A cruising controller to maintain a user-set desired speed if a preceding vehicle is absent.
• A gap-regulating controller to maintain a constant time gap with its predecessor in car-following situations.

(1) Cruising controller
The control objective of the cruising mode is to maintain the user-desired speed when the preceding vehicle is absent or far away. The acceleration of a cruising vehicle is modeled as: Where, the control gain 0 k is a parameter to determine the rate of speed error for acceleration, set v is the driver's desired speed and , 1 n k v  is the speed of vehicle n at time step k . The value of 0 k is assumed as 0.4 s −1 according to reference [13] .

(2) Gap-regulating mode
In the gap-regulating mode, the car-following response of the first truck in the platoon is n k e is the gap error of vehicle n at time step k . An existing study found that the vehicle acceleration depends on the gap error and the speed difference with the preceding vehicle, where their feedback gains 1 k and 2 k are 0.23 s −2 and 0.07 s −1 , respectively [14] .
For the following trucks in the platoon, their speeds are calculated by the speed in a previous time step , 1 n k v  , the gap error , 1 n k e  in a previous time step and the corresponding derivative. The following form is used for calculation.
The gap error , 1 n k e  is calculated by using the following form: is the inter-truck spacing, des t is the desired time gap, L is the truck length, and 0 d is the spacing margin [16] .
The other model parameters for traffic flow simulation are listed in the following table.

Simulation loop and outputs
Based on the input data and the traffic flow models, the behaviors of independent trucks or the trucks in a platoon are simulated. For each simulation case, one hour is spent on warm-up. After the warm-up period, different classes of vehicles are generated for simulation. The total number of the generated vehicles depends on the input traffic volume. An additional module is implemented in the simulation loop to guide vehicle generation. This module ensures that the hourly number of vehicles passing through a road section in the simulation matches the recorded traffic data.
After the simulation, the trajectory data (including vehicle ID, traveling time, location, speed, acceleration/deceleration) of each vehicle are recorded. The traffic flow for each road section is generated and used to determine the loading gap distribution between different trucks. The typical patterns of vehicle trajectories in the two traffic modes are illustrated in Fig. 2-2, where the blue lines represent the trajectories of cars, and the red lines represent the trajectories of trucks. Fig. 2-2 indicates that the red lines (i.e., trucks) distribute evenly under normal operation, implying that the trucks generally operate freely and independently. However, the red lines in platooning tend to agglomerate due to the trucks running close to each other. In platooning, gaps between adjacent trucks are relatively small, causing the trucks' trajectory lines to overlap.

Supplementary Method 3. Fuel consumption model for truck platooning
The fuel consumption of the vehicle is directly related to the GHG emission of the vehicle-road infrastructure system. In this research, a fuel consumption model is developed and used to evaluate the fuel economy of the vehicle in a platoon or not. Based on the fuel consumption, the emission during vehicle operation is assessed. The detailed procedures for developing the fuel consumption model are introduced as follows.

Aerodynamics of truck platooning
The aerodynamics of trucks running in platooning mode differ from those of an independent truck due to the drag-reduction effects from the lead truck, which has to overcome most of the drag resistance while the remaining trucks in the platoon are subject to much less air resistance [17][18][19] . The reductions in drag resistances of the non-lead trucks help lower the required work from truck engines, thereby decreasing the overall fuel consumption of a truck platoon [20][21][22][23] . The fuel-saving potential of a truck platoon is highly dependent on the drag resistance it has to overcome. Therefore, to assess the fuel economy of a truck platoon, it is necessary to investigate the aerodynamics of the trucks in the platoon.
Computational Fluid Dynamics (CFD) simulation has been commonly applied to evaluate the aerodynamics of vehicles [24] . This research also used the CFD simulation tool to investigate the truck platoon's aerodynamic behaviors. The simulation cases consist of three types of trucks with different dimensions and axle configurations, four types of separation distances, and five types of truck numbers in a platoon. These combinations enable us to evaluate the aerodynamics of different forms of truck platoons comprehensively. The schematic diagrams of the three types of trucks assessed in this research are shown in Fig. 3-1. The numerical models in CFD simulation are developed based on the adopted truck configurations, and the 1/8 scale truck model is used to save computation time. The overall computational domain was determined as 6H×16L×10W, as shown in Fig. 3-2. The symbols H, L, and W in the figure refer to the height, length, and width of the truck model, respectively. The boundary conditions of the computational domain are determined as follows: The inlet and outlet are the velocity inlet and the pressure outlet, respectively. The truck body and the stationary ground are set as the non-slip wall conditions, while the moving wall has the same velocity as the inlet. The rest of the boundaries use symmetric boundary conditions. As for the meshing strategy, the mixed grids are assigned for the computation domain. The prism layers are used for the wall boundaries, whilst the hexahedral grids are used for the rest of the domain. The trimming technique is applied to transit the areas between the prism layers and the hexahedral grids. To improve the computing efficiency, fine mesh is used around the truck, while relatively coarse mesh is used for the area far away from the truck. Specifically, the mesh size for the prism layer is 1 mm, while that at the truck surface is 5 mm.
Based on simulations, the aerodynamics of the trucks running separately or in a platoon are determined and compared. Example comparison results are presented in Fig. 3-3, where the calculation results of two-axle trucks are used for illustration. As shown in Fig. 3-3 (a) and (b), the air pressure coefficients of the independent truck differ from those of the trucks in a platoon. Clearly, the pressure coefficients of the middle truck or trailing truck in the platoon are lower than those of an individual truck, indicating that platooning reduces the air resistance of the non-lead trucks. Similar results can be observed for other simulation cases. They are not shown here for the interest of brevity. Based on the simulation results, the drag coefficients (defined as D C ) for the three independent trucks are calculated and compared, as shown in Fig. 3-4. As expected, the D C increases with the truck size. A truck with a large dimension is subjected to more air resistance and a higher drag coefficient than a small truck. The drag coefficients of the trucks located at different positions of a platoon (i.e., lead position, middle position, trailing position) are illustrated in Fig. 3-5. In this typical case, the platoon consists of three successive two-axle trucks. The separation distance between two adjacent trucks is 0.2 L ( L = truck length). Fig. 3-5 indicates that D C of the lead truck is the highest, followed by the middle truck and then by the trailing truck. Not only the middle truck and trailing truck in the platoon experience less air resistance as compared with the lead truck, but the D C of the lead truck (0.359) is lower than that of the independent truck (0.450 as shown in Fig. 3-4). Therefore, platooning can reduce the drag coefficients of the entire fleet. This finding also applies to platooning configurations consisting of the different truck types (i.e., four-axle truck and six-axle truck) or truck numbers (e.g., four trucks in a platoon or five trucks in a platoon). [ Where, S is the separation distance, L is the truck length, and a , b and c are the fitting coefficients which are summarized in Table 3

Prediction of fuel consumption savings for trucks in a platoon
Section 3.1 reveals that the drag coefficients of the trucks in a platoon are lower than those of independent trucks. Drag coefficient is one of the main factors affecting a truck's fuel consumption. Consequently, trucks in a platoon would consume less fuel than those independent trucks. In this section, models for predicting the fuel consumption of different truck configurations will be developed. The models will turn the effect of drag coefficient reduction into fuel saving.
An operating truck is subjected to several types of resistance, including inertia, rolling resistance, and air resistance [25][26][27] . Inertia is caused by the acceleration or deceleration of the truck, while the rolling and air resistances come from the road and the air-fluid around the truck, respectively. The requested engine power to overcome the resistances can be expressed by the following equation: Where, P is the engine power,  is the efficiency coefficient, r F is the rolling resistance, D is the air resistance, I refers to the inertia-resistance-related component, 0 r is the coefficient of road resistance, m is the mass of the truck,  is the density of the air, v is the velocity of the truck relative to the air-fluid, and A is the front area of the truck.
The fuel consumption of a truck can be calculated based on its engine power using: Where, FC is the fuel consumption, bsfc is the brake-specific-fuel-consumption of the engine. Based on this form, the saving rate of the truck's fuel consumption ( FC  ) in a platoon is calculated as follows: ) can be calculated as follows: Where, D C  is the saving rate of the drag coefficient that can be calculated based on Equation (3.1). Combining Equations (3.5) and (3.6), the final form for calculating the fuel-saving rate for a truck in a platoon is derived as: Where, a , b and c refer to the fitting coefficients which are shown in Table 3-1.

Calibration of the fuel consumption model
Models developed in Section 3.2 are based on the truck's aerodynamics obtained from CFD simulations. In real situations, a truck's fuel consumption is also influenced by other factors such as engine temperature, lateral offset, and environmental factors such as heat and humidity. As these factors also play a role in fuel consumption, errors may arise if they are excluded in fuel consumption prediction. Hence, Equation (3.7) needs to be calibrated via field-measured data that reflect those influencing factors. The data used for calibration comes from the Partially Automated Truck Platooning (PATP) project sponsored by the FHWA [21,28] . This project tested the fuel economy of a three-vehicle truck platooning system in the field. The test was conducted in accordance with the SAE J1321 Fuel Consumption Test Procedure [29] . Specifically, a portable tank filled with fuel was installed on the truck and weighed before the trip. After the trip, the weight of the tank was measured again, and the fuel consumption of the trucks was accurately determined. Fuel consumption of independent trucks and platooned ones were compared. The information on the truck, road, and air conditions used in the PATP project are summarized in Table 3-2.  [28] .

Parameters Units Values
Truck mass (m) kg 29400 Where, S is the separation distance, L is the truck length, 0 r is the coefficient of road resistance, m is the mass of the truck,  is the density of the air, v is the velocity of the truck relative to the air fluid, A is the front area of the truck, and a , b and c are the fitting coefficients which have been summarized in Table 3-1.
The fuel savings predicted with the updated model are further compared with the measured ones in Fig. 3-8. Clearly, the predicted fuel savings of the platoon fit very well with the field-measured ones. As a result, the calibrated model (Equation (3.9)) is reliable enough to estimate the actual fuel savings of the platooning trucks.  Table 2-1. Based on the data in Table 2-1 and Equation (3.9), the fuel-saving rate of a truck in a platoon can be calculated, and the fuel consumption of the truck can be calculated using the following model:  Table 3-3 [30] . It is noted that the average miles per gallon of fuel (MPG) for different types of trucks are recorded in the table. With those MPG data, the fuel consumption of an independent truck is determined. Passenger cars are assumed to have the same fuel economy under normal traffic and truck-platooning modes, considering that the aerodynamic behaviors of passenger cars on highways are much less affected by truck platooning.  [30] .

Supplementary Method 4. Damage model for road section under truck platooning
Road pavement is continuously damaged by repetitive truck loads during its service life. As a result, it needs to be periodically maintained or even rehabilitated. The maintenance or rehabilitation work such as sealing the cracks, patching the holes, or milling the old roads, generates GHG emissions. In evaluating the life-cycle emission of road, it is essential to consider those maintenance/rehabilitation activities. The maintenance/rehabilitation period for the road pavement is dependent on its damage state. An elevated damage level demands more frequent maintenance or rehabilitation.
Commonly observed damages on road pavements can be divided into two groups: permanent deformation (also known as rutting) and cracking-related distresses (fatigue cracking, potholes, etc.). The LTPP database suggests that cracking-related distress is the predominant type of pavement failure. Therefore, this research focuses on cracking-related distresses only. The damage state assessment of asphalt pavement is complicated due to the fatigue-healing behaviors of asphalt concrete (AC) layers. Damage accumulation within an AC layer is influenced by the time gap (i.e., rest period) between adjacent loads [31][32][33][34] . A longer rest period promotes a more appreciable healing effect on the asphalt layer and thus results in less damage accumulation. In the truck platooning mode, however, the rearward truck follows the preceding truck closely, and the distance between the adjacent trucks is very narrow (roughly 5m). Consequently, the rest periods between trucks in the platooning mode are quite different from those experienced during a normal operation. Differences in the rest period lead to different pavement damage accumulation processes, which further influence the maintenance/rehabilitation frequencies and the overall emissions from the road. This chapter aims to introduce a damage model for road pavement under different truck operation conditions. With the developed model, the damage state of a road pavement under the normal or platooning truck loading mode can be determined. The information is further used to assist in scheduling maintenance/rehabilitation activities and calculating the corresponding emissions.

Laboratory loading tests
Both laboratory and field loading tests were conducted in this research to simulate the damage effects of truck loads on road pavements. The applied laboratory test is called the four-point bending (4PB) fatigue loading test. Three types of loading waveforms were used in the 4PB fatigue tests. They correspond to the strain waves in road pavements induced by single-axle, tandem-axle and tridem-axle configurations of a truck, respectively. The schematic diagram of the applied loading waves is shown in Fig. 4-1. A total of three temperature levels (20℃, 10℃ and 0℃) were considered in 4PB tests to simulate different environmental conditions. At each test temperature, three different strain levels were applied. The various strain levels were used to characterize the influences of truck weights on road damage. In addition, the rest period (time interval between two adjacent axle loadings) is introduced in the 4PB test to investigate the healing effect of the rest period on the asphalt concrete's damage ( Fig. 4-1). The applied rest period includes four magnitudes: 0s, 0.1s, 0.5s, and 1s. For each loading scenario, two to six parallel fatigue tests were conducted to control test variability. The photographs of the apparatus and specimens in the 4PB loading test are presented in Fig. 4-2. Based on the 4PB test results, the fatigue lives of AC samples under various strain waves, rest periods, and temperatures were analyzed and compared. The typical comparison results are shown in Fig. 4-3. The fatigue lives of the AC samples vary with strain levels approximately in a linear mode on the log-log scale, regardless of loading waveforms. The specimen's fatigue life is reduced with an increase in strain level. At the same strain level, the fatigue life of the specimen subject to the single-axle wave is higher than that subject to the tandem-axle wave, and the fatigue life associated with the tridem-axle wave is the lowest. The results indicate that loading waveforms induced by the multi-axles result in much lower fatigue lives of the samples than those of samples subject to single-axle load. This is because the multi-axle waveform has multi pulse peaks with a longer loading duration. It can thus be concluded that the multi-axle loading waveform inflicts more fatigue damage to the AC sample than the single-axle waveform does.

Fig. 4-3. The comparisons of fatigue lives of AC samples at (a) three loading waveforms, (b) four rest periods, and (c) three temperatures.
At the same strain level, the fatigue lives measured with rest periods (0.1s, 0.5s, 1s) are apparently higher than those measured with no rest period (0s), as shown in Fig. 4-3(b). This phenomenon has been proven in previous studies [35][36][37][38][39] . The increase in fatigue life with a longer rest period is attributed to its healing effects on AC material. With the rest period, AC can resist more fatigue loading repetitions as a portion of fatigue damage can be healed. A longer rest period leads to a longer fatigue life for the specimen, but the fatigue life cannot be extended indefinitely with the rising rest period. In addition, the fatigue lines (the plots of fatigue life versus strain) at different rest periods are approximately parallel to each other, suggesting that the effects of strain level on the fatigue life are nearly unaffected by rest period level. As indicated in Fig. 4-3(c), temperature also has an apparent effect on the AC's fatigue life, irrespective of loading waveforms. Higher temperature is associated with a longer fatigue life of the specimen if the strain level is kept the same. The slopes of the Lg (fatigue life) versus Lg (strain level) lines are similar at different temperatures. This implies that the impacts of the strain levels on the fatigue life are almost unaffected by the test temperature levels.
Two advanced analysis methods namely the dissipated energy (DE) method and the viscoelastic continuum damage (VECD) method were further used to process the fatigue test results. The relationships between the initial DE (IDE) and the fatigue life of AC material obtained at different rest periods and temperatures are presented in Fig. 4-4. It is seen that the fatigue life of AC varies approximately linearly with IDE on the log-log scale. An increase in IDE is associated with a reduction in fatigue life, and the trends are the same for the three types of loading waveforms. This indicates that the IDE vs. fatigue life relationships tend to be unaffected by the waveform.  This implies that, at the same level of IDE, a specimen's fatigue life will be extended with the rest period being increased. The healing effect associated with the different rest periods apparently contributes to the prolonged fatigue life of a specimen under the same level of IDE. However, the effect of temperature on the IDE-fatigue life relationships is not so obvious, as seen in Fig.  4-4 (c). The IDE-fatigue life line at 20℃ coincides with that at 0℃, and both are located slightly above that at 10℃.
In the VECD method, the pseudo stiffness named C and the damage parameter named S are applied to quantify the fatigue damage evolution of a material [40][41][42][43] . The C-S curves obtained at different strain waveforms and rest periods are presented in Fig 4-5. It can be observed that the C-S curves obtained at different strain levels generally collapse with each other, which supports the common observation that the C-S curve of AC is free from the effect of loading amplitude. However, there are obvious differences among the C-S curves derived from the three types of loading waveforms, as shown in Fig. 4-5(a). The C-S curve induced by the single-axle wave is located at the top position, followed by the one corresponding to the tandem-axle wave and then by the one corresponding to the tridem-axle wave. Such positions are consistent with the fact that the single-axle wave is associated with higher fatigue life than the tandem-axle or tridem-axle waves. As shown in Figs. 4-5(b)~(d), the rest period has a remarkable effect on the C-S curve of the AC, regardless of strain wave type. Generally, the curves obtained from the tests with rest periods are positioned above that without rest period (i.e., rest period=0s), especially at the initial stage of the test. This phenomenon reflects the fact that the damage evolution of a specimen slows down with the available rest period, agreeable with the previous observations that the rest period helps heal the mixture from damage and prolongs its fatigue life [34,37,38] .
From the above discussions, it can be concluded that strain level, loading waveform, rest period, and temperature all have noticeable effects on a specimen's fatigue life. The fatigue life increases with a decreasing strain level, a decreasing axle number, an increasing rest period, and an increasing temperature. Therefore, all these influencing factors are included in developing the fatigue life prediction model for AC. In addition, the initial stiffness modulus of AC is included in the model, to take into account the impact of AC's nature on its fatigue behaviors. Ultimately, the following function was developed to predict the fatigue life of AC material under different loading scenarios.
( ) Where, f N is the fatigue life,  is the strain level (με), E is the initial stiffness modulus (MPa), T is the temperature (℃), RP is the rest period (s), and a , b , c , d , f are fitting parameters.
The fitted model parameters with different strain waveforms are summarized in Table 4-1.
The predicted fatigue lives based on the model are compared with the measured ones in Fig.  4-6. It can be seen that the developed fatigue life prediction model well characterizes the relationships between fatigue life and different influencing factors. The correlation coefficients (R 2 ) for the models concerning tandem-axle wave and tridem-axle wave exceed 0.8, while that for the model concerning single-axle wave is relatively low at 0.723.

Field loading tests for calibration
While the test and analysis results above indicate that the fatigue life of an AC sample prepared and tested in laboratory conditions can be well predicted, the fatigue behaviors of AC layer in field pavements can be quite complicated due to variations in actual dimensions and stress states. Therefore, laboratory models need to be calibrated. Full-scale pavement loading tests were used to calibrate the laboratory fatigue models [44] . The full-scale tests were conducted on a field road pavement with a large loading facility named MLS 66. The MLS 66, as presented in Fig. 4-7, is the most advanced truck-load simulator. Based on this powerful facility, a total of 1,000,000 field loading repetitions were applied on the road pavement to simulate the effects of truck loads. During the tests, the damage states of the field road pavements were continuously monitored using the portable seismic property analyzer (PSPA). AC samples were also extracted from the field road pavements and assessed by laboratory tests. By comparing the field measurements and the laboratory test results, the shift factor (SF) between the field and the laboratory conditions was determined to be 1.382, with field fatigue life for AC layer being larger than laboratory one. With this shift factor, the model as presented in Equations (4.1) is adjusted to describe the fatigue behaviors of the field AC layer. The calibrated prediction model is shown as follows.
( ) Where, f N is the fatigue life,  is the strain level (με), E is the initial stiffness modulus (MPa), T is the temperature (℃), RP is the rest period (s), and SF is the shift factor which has a value of 1.382.

Road response prediction model
As discussed in Section 4.1, the fatigue life of an AC material is highly dependent on load-induced strain levels, along with other influencing factors. Therefore, the strain response of field pavement needs to be determined. In this research, the pavement's strain response is calculated based on the multi-layer elastic (MLE) theory, which has been widely used in road pavement design specifications including the U.S. mechanistic-empirical pavement design guide (MEPDG) and China's specification [45,46] . The overall framework for pavement response calculation is presented in Fig. 4-8.   Fig. 4-8. The overall framework for road response calculation.
As shown in Fig. 4-8, information on road and truck load is needed as input data for pavement response calculation. Road information includes road structures (arrangement of structural layers & thickness), material properties (modulus & Poisson's ratio), and environmental conditions (temperature). These data have been introduced in Supplementary Method 1. Truck load information includes truck classification, axle configuration, gross weight and tire contact area, which has been introduced in Section 1.2. The spatial-temporal distributions of trucks under normal traffic mode and truck-platooning mode have been determined in Section 2.4. With the above input data, the strain field in a road under truck loading is calculated using the MLE theory. The strains at different depths and horizontal positions (i.e., the relative positions to the loading area) are calculated to determine the critical position with the maximum strain. The evaluated pavement positions regarding different axle configurations are presented in Fig. 4-9. Wheel wander, which refers to the lateral offset of a vehicle from the wheel path center, is also an important factor that influences the critical strain of the pavement. The lateral offsets of non-platooning trucks (i.e., human-driven trucks) generally follow a normal distribution, as reported in the NCHRP's research [46] . On the other hand, platooning trucks can have their lateral offsets more precisely designed, thanks to the superior control ability of platooning technology. Existing studies have proposed several wheel wander modes for platooning trucks to reduce the damaging impacts of truck loads on the pavement [47,48] . In this research, we considered two of these modes: (1) Platooning mode 1: Platooning trucks are controlled to load evenly on three sub-lanes of the wheel path to lower the critical strain at the wheel center.
(2) Platooning mode 2: Platooning trucks are controlled to load evenly across the wheel path to distribute the truck damage at the wheel center.
The schematic diagrams of the wheel wander distribution modes for non-platooning and platooning trucks are shown in Fig. 4-10. Each platooning mode is further divided into two sub-controlling modes:  Sub mode 1: All platooning trucks are randomly assigned to follow the specially designed distribution mode.  Sub mode 2: Trucks within a platoon follow the same driving path, while trucks in different platoons are assigned to follow the specially designed distribution mode.
The schematic diagrams of the two sub modes are presented in Fig. 4-11. It is seen that in sub mode 2, the leading truck in a platoon resists most of the drag resistance, while the trailing ones experience less air resistance. However, in sub mode 1, the trailing trucks may still face noticeable air resistance due to the lateral offset from the driving path of the leading truck. Based on the distributions described above, the critical strain responses of the road pavement under non-platooning and platooning trucks were calculated. The resulting critical strain values were then used in fatigue life prediction models to estimate the fatigue damage of the road pavement under a specific axle load, along with the shift factor. It was found that the combination Platooning mode 2 + Sub mode 2 is the most beneficial strategy for reducing fatigue damage caused by platooning trucks on the road. This combination is also the best solution for reducing air resistance and fuel consumption of platooning trucks. Therefore, the Platooning mode 2 + Sub mode 2 strategy is ultimately adopted as the wheel wander control method for platooning trucks in this research.

Development of the road damage model
Where, i n is the total number of load repetitions in the i-th block of truck loading, k is the total number of trucks, An example procedure of calculating pavement damage accumulation is illustrated in Fig. 4-12, where the terms S, Ta and Tr refer to the respective single axle, tandem axle and tridem axle, and RP refers to the rest period. Based on this procedure, the damage state evolutions of the road pavement under different traffic modes (normal vs. truck-platooning) are determined to assist in scheduling maintenance or rehabilitation activities and calculating the corresponding emissions.

Supplementary Method 5. GHG emission and cost model for the integrated vehicle-road infrastructure system
The outputs from Supplementary Methods 1~4 are used to calculate the GHG emissions (costs) of the integrated vehicle-road infrastructure system under normal mode and truck-platooning mode. The GHG emission (cost) models used for calculation are introduced in detail in this chapter. The overall framework of the GHG emission calculation model is presented in Fig. 5-1. As noted, GHG emissions of the integrated system consist of emissions from road infrastructure and vehicles. Emissions from road infrastructure are divided into two portions to facilitate analysis: emissions from the initial construction stage and those from the road maintenance work after construction (i.e., maintenance, rehabilitation and reconstruction). Road emissions at the initial construction stage include emissions from material production, material transport and construction equipment operations. By contrast, road emissions at the maintenance stage are generated from the maintenance material production & transport and maintenance equipment operations. The end-of-life (EOL) processing of road materials (i.e., milling and transport) is also considered in the maintenance stage through transport and equipment operation modules. In addition, traffic disruptions due to lane closure during maintenance work also account for the maintenance stage's emissions. Traffic disruptions, including deceleration, acceleration, slowing down and even queuing of vehicles, produce extra emissions as compared with those generated in normal vehicle operations. Even though such extra emissions are directly from traveling vehicles, they are only generated during road maintenance work and thus are assigned to road infrastructure emissions. The vehicle emissions are attributed to those from vehicles traveling on the road sections, which are estimated based on the vehicles' fuel consumption. The two sources of emissions are closely related. Firstly, vehicle emission is dependent on road performance, such as surface roughness (defined as the international roughness index, IRI). The high roughness of a road increases vehicle emissions. As a result, the road performance data is incorporated into the vehicle emission model. Secondly, vehicle emission models are used to calculate the additional emissions from road-work-related traffic disruptions.

The overall framework of GHG emission (Cost) models
The cost model for the vehicle-road system is similar to that of the emission model, except that the emission intensity in the framework is replaced with the cost intensity. Hence, the framework of the cost model will not be separately introduced. The detailed calculation models for road infrastructure and vehicle emissions are presented as follows.

GHG emissions from road infrastructure 5.2.1 GHG emissions from the initial construction stage and the maintenance stage
The emissions in this module are generated during the initial road construction, the road maintenance and the EOL processing of road materials, including the material production, material transport and equipment operations. The following equation is used to calculate the emissions from road construction and maintenance work.
Where, CME GHG is the emission from the road construction, road maintenance or EOL processing activity, CMEi f is the unit emission intensity of the i-th activity, CMEi q is the quantity of the i-th activity. The quantity of the construction, maintenance or EOL processing activity is calculated based on road dimensions (e.g, pavement thickness, lane width, lane length, maintenance length). The unit emission intensities are are summarized in Table 5-1. is the unit emission intensity at a normal traffic state. The traffic disruption states on the road during construction work are estimated using the RealCost software [54] . The input parameters for the RealCost are summarized in Table 5-2. Based on the traffic disruption states, additional emissions during road work are estimated by combining the corresponding emission intensity data, which are summarized in Table 5-3.

Items Emission intensities
Deceleration/Acceleration process 1 : From normal speed to work zone speed/From work zone speed to normal speed 1 The emission intensities are determined according to references [55,56] .
2 The emission intensities of vehicles at the slowing-down state or queuing state are estimated using Equations (5.7)~(5.9), which are developed based on references [50,57,58] .

GHG emissions from normal vehicle operations
Vehicle emissions are estimated based on the vehicles' fuel consumption. Two traffic modes namely normal and truck-platooning modes are included in this research. In truck platooning mode, the fuel consumption of trucks is saved due to reductions in air resistance (see Supplementary Method 3 Equation (3.9)). In addition, to consider the effect of road roughness on vehicle emissions, the IRI data is also incorporated into the vehicle emission model. The following model is developed to predict vehicle emissions. 1 ( is the variation rates of vehicle fuel consumption due to road's IRI performance. IRI  is the gap between the road's actual IRI and the baseline IRI (1.0m/km is used in this research).
As mentioned before, the fuel consumption ( i FC ) of a vehicle is determined according to the statistical Motor-Vehicle Travel (VMT) data from FHWA (see Supplementary Method 3  Table 3-3), which represents the actual fuel economy of vehicles in the U.S. The unit emission intensities and the variation rates of fuel consumption due to road roughness ( _ IRI i FC  ) are determined according to previous research, as shown in Table 5-4. 1 The fuel consumption of vehicles is determined according to FHWA statistical data [57] .
2 The emission intensities corresponding to fuel consumption are determined according to the reference [50,59] . 3 The variation rates of fuel consumption due to IRI are determined according to the reference [60,61] .
Based on the models in Sections 5.2 and 5.3, the total road-related and vehicle-related GHG emissions are calculated, which represent the overall emissions from the integrated vehicle-road system. The calculation procedure of the cost of the vehicle & road system is similar to that of the emission, and hence is not discussed. The cost intensities used in the cost model are summarized in Table 5-5. In addition to the cost components shown in Table 5-5, drivers also have to bear the costs of tire wear-and-tear (W&T) and vehicle repair-and-maintenance (R&M). Both these costs depend on the pavement surface condition (i.e., IRI). A better pavement condition generally leads to lower costs. To calculate the W&T and R&M costs, Equations (5.14) and (5.15) were developed according to the data shown in the literature [65] .